DeepOBS test problem class for the VGG 19 network on Cifar-100.
The CIFAR-100 images are resized to
224to fit the input dimension of the original VGG network, which was designed for ImageNet.
Details about the architecture can be found in the original paper. VGG 19 consists of 19 weight layers, of mostly convolutions. The model uses cross-entroy loss. A weight decay is used on the weights (but not the biases) which defaults to
- batch_size (int) -- Batch size to use.
- weight_decay (float) -- Weight decay factor. Weight decay (L2-regularization)
is used on the weights but not the biases.
The DeepOBS data set class for Cifar-100.
A tensorflow operation initializing the test problem for the training phase.
A tensorflow operation initializing the test problem for evaluating on training data.
A tensorflow operation initializing the test problem for evaluating on test data.
A tf.Tensor of shape (batch_size, ) containing the per-example loss values.
A scalar tf.Tensor containing a regularization term.
A scalar tf.Tensor containing the mini-batch mean accuracy.
Set up the VGG 19 test problem on Cifar-100.